Multisource Composite Kernels for Urban-Image Classification
Multisource Composite Kernels for Urban-Image Classification
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DOI：
10.1109/lgrs.2009.2015341
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发表时间：
2010
影响因子：
4.8
通讯作者：
D. Tuia;F. Ratle;A. Pozdnoukhov;Gustau Camps-Valls
中科院分区：
文献类型：
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作者：
D. Tuia;F. Ratle;A. Pozdnoukhov;Gustau Camps-Valls
这封信介绍了超高分辨率图像的高级分类方法。通过在支持向量机中使用复合核，可以利用有效的多源信息（光谱和空间）。分析了考虑不同光谱和空间信息源的核的加权求和，并将其与经典方法（例如纯光谱分类或使用单个向量中的所有特征的堆叠方法）进行比较。解决了模型选择问题，以及不同核在加权求和中的重要性。
This letter presents advanced classification methods for very high resolution images. Efficient multisource information, both spectral and spatial, is exploited through the use of composite kernels in support vector machines. Weighted summations of kernels accounting for separate sources of spectral and spatial information are analyzed and compared to classical approaches such as pure spectral classification or stacked approaches using all the features in a single vector. Model selection problems are addressed, as well as the importance of the different kernels in the weighted summation.